CDP quality checks

Version: 1st extract

• About
• Data Quality report

CDP workflow

This report is meant to run a data quality check through out the CDP questions that are related to **Waste data**.

This is the first part of the whole process that ends having the dataset uploaded in the Data Warehouse and with it, available to use by tools like Qlik for data consumption like dashboards or reports.

How to read the Quality Checks report?

Number of cities that responded per question

Number of cities that responded per indicator
Indicator	Responses
	Amount	Percentage
Percentage of the diverted solid waste generated that is recycled (%)	59	75.6%
Percentage of the diverted solid waste generated that is reused (%)	23	29.5%
Percentage of the total solid waste generated that is diverted away from landfill and incineration (%)	61	78.2%
Percentage of the total solid waste generated that is utilized for waste to energy (%)	48	61.5%
Percentage of waste collected where separation at source is taking place (%)	40	51.3%
Percentage of wastewater safely treated to at least secondary level (%)	44	56.4%
Total amount of solid waste generated (tonnes/year)	76	97.4%
Total annual amount of food waste produced in the jurisdiction (tonnes/year)	52	66.7%
Volume of wastewater produced within the jurisdiction boundary (megalitres/year)	51	65.4%

Data Quality Report
QA checks - Check columns structure tibble waste cdp questionsWARN 0.00 STOP — NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
	15	Check that columns is numeric col_is_numeric()	▮response_orig	—		✓	1	0 0.00	1 1.00	●	—	—	—
	16	Check that columns is numeric col_is_numeric()	▮response_suggested	—		✓	1	0 0.00	1 1.00	●	—	—	—
	17	Check that column in integer col_is_integer()	▮year_orig	—		✓	1	0 0.00	1 1.00	●	—	—	—
	18	Check that column in integer col_is_integer()	▮year_suggested	—		✓	1	0 0.00	1 1.00	●	—	—	—
2024-07-01 17:20:46 -03 < 1 s 2024-07-01 17:20:46 -03

Data Quality Report
QA checks - Check question value’s quality tibble Waste cdp questionsWARN 0.00 STOP — NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
	2	Check that year col is between 2000 and current 2024 col_vals_between()	▮year_suggested	[2,000, 2,024]		✓	488	487 0.99	1 0.01	●	—	—	CSV
	4	Check that values containg numbers only col_vals_regex()	▮response_suggested	^[0-9]		✓	488	487 0.99	1 0.01	●	—	—	CSV
	6	Check that values containg numbers only col_vals_regex()	▮year_suggested	^[0-9]		✓	488	487 0.99	1 0.01	●	—	—	CSV
	7	Check comments where the word wrong or probably/likely wrong is mentioned col_vals_equal()	▮response_wrong	0		✓	488	468 0.96	20 0.04	●	—	—	CSV
	8	Check comments where the word wrong or probably/likely wrong is mentioned col_vals_equal()	▮response_prob_likely_wrong	0		✓	488	481 0.99	7 0.01	●	—	—	CSV
	9	Check that percentage indicator's values are between 0 and 100 col_vals_between()	▮response_orig	[0, 100]		✓	302	289 0.96	13 0.04	●	—	—	CSV
	10	Check that percentage indicator's values are between 0 and 100 col_vals_between()	▮response_suggested	[0, 100]		✓	302	298 0.99	4 0.01	●	—	—	CSV
2024-07-01 17:20:46 -03 < 1 s 2024-07-01 17:20:46 -03

Check columns thar seem to be merged (they appear as lists in the dataframe)

Check values as numbers, clean them and check if any value got lost

• The number of columns in your data match the number of variables you have in your data dictionary (i.e., no variables were accidentally dropped). Similarly, there should be no unexpected missing data for variables (i.e., if the data was collected, it should exist in your dataset).